334 research outputs found
Sparse Coding Predicts Optic Flow Specificities of Zebrafish Pretectal Neurons
Zebrafish pretectal neurons exhibit specificities for large-field optic flow
patterns associated with rotatory or translatory body motion. We investigate
the hypothesis that these specificities reflect the input statistics of natural
optic flow. Realistic motion sequences were generated using computer graphics
simulating self-motion in an underwater scene. Local retinal motion was
estimated with a motion detector and encoded in four populations of
directionally tuned retinal ganglion cells, represented as two signed input
variables. This activity was then used as input into one of two learning
networks: a sparse coding network (competitive learning) and backpropagation
network (supervised learning). Both simulations develop specificities for optic
flow which are comparable to those found in a neurophysiological study (Kubo et
al. 2014), and relative frequencies of the various neuronal responses are best
modeled by the sparse coding approach. We conclude that the optic flow neurons
in the zebrafish pretectum do reflect the optic flow statistics. The predicted
vectorial receptive fields show typical optic flow fields but also "Gabor" and
dipole-shaped patterns that likely reflect difference fields needed for
reconstruction by linear superposition.Comment: Published Conference Paper from ICANN 2018, Rhode
Human identification via unsupervised feature learning from UWB radar data
This paper presents an automated approach to automatically distinguish the identity of multiple residents in smart homes. Without using any intrusive video surveillance devices or wearable tags, we achieve the goal of human identification through properly processing and analyzing the received signals from the ultra-wideband (UWB) radar installed in indoor environments. Because the UWB signals are very noisy and unstable, we employ unsupervised feature learning techniques to automatically learn local, discriminative features that can incorporate intra-class variations of the same identity, and yet reflect differences in distinguishing different human identities. The learned features are then used to train an SVM classifier and recognize the identity of residents. We validate our proposed solution via extensive experiments using real data collected in real-life situations. Our findings show that feature learning based on K-means clustering, coupled with whitening and pooling, achieves the highest accuracy, when only limited training data is available. This shows that the proposed feature learning and classification framework combined with the UWB radar technology provides an effective solution to human identification in multi-residential smart homes
A Neural Spiking Approach Compared to Deep Feedforward Networks on Stepwise Pixel Erasement
In real world scenarios, objects are often partially occluded. This requires
a robustness for object recognition against these perturbations. Convolutional
networks have shown good performances in classification tasks. The learned
convolutional filters seem similar to receptive fields of simple cells found in
the primary visual cortex. Alternatively, spiking neural networks are more
biological plausible. We developed a two layer spiking network, trained on
natural scenes with a biologically plausible learning rule. It is compared to
two deep convolutional neural networks using a classification task of stepwise
pixel erasement on MNIST. In comparison to these networks the spiking approach
achieves good accuracy and robustness.Comment: Published in ICANN 2018: Artificial Neural Networks and Machine
Learning - ICANN 2018
https://link.springer.com/chapter/10.1007/978-3-030-01418-6_25 The final
authenticated publication is available online at
https://doi.org/10.1007/978-3-030-01418-6_2
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